Relation Extraction from Biomedical and Clinical Text: Unified Multitask Learning Framework

TL;DR

This paper introduces a unified multitask learning framework with structured self-attention and adversarial training for extracting various biomedical relations, significantly improving over existing deep learning baselines.

Contribution

It is the first to apply structured self-attentive networks with adversarial learning to biomedical relation extraction within a multitask learning framework.

Findings

MTL framework improves relation extraction accuracy

Proposed models outperform baseline deep learning methods

Single-task models with dependency path embeddings are competitive

Abstract

To minimize the accelerating amount of time invested in the biomedical literature search, numerous approaches for automated knowledge extraction have been proposed. Relation extraction is one such task where semantic relations between the entities are identified from the free text. In the biomedical domain, extraction of regulatory pathways, metabolic processes, adverse drug reaction or disease models necessitates knowledge from the individual relations, for example, physical or regulatory interactions between genes, proteins, drugs, chemical, disease or phenotype. In this paper, we study the relation extraction task from three major biomedical and clinical tasks, namely drug-drug interaction, protein-protein interaction, and medical concept relation extraction. Towards this, we model the relation extraction problem in multi-task learning (MTL) framework and introduce for the first time the concept of structured self-attentive network complemented with the adversarial learning approach for the prediction of relationships from the biomedical and clinical text. The fundamental notion of MTL is to simultaneously learn multiple problems together by utilizing the concepts of the shared representation. Additionally, we also generate the highly efficient single task model which exploits the shortest dependency path embedding learned over the attentive gated recurrent unit to compare our proposed MTL models. The framework we propose significantly improves overall the baselines (deep learning techniques) and single-task models for predicting the relationships, without compromising on the performance of all the tasks.